Analysis of failure data using a purpose-built system of statistical models combined with LLM assistance. No infrastructure access required. Security-first by design.
↓ View sample reportInstead of 3 hours of post-mortem — export the log, share the report. Your client sees structured analysis with numbers, not a summary email.
We show which 3 incident types drive 80% of your problems. Fix those — and the repeating pattern stops.
No dedicated reliability engineer? Get a structured picture of your risks without hiring one.
Any format works. Use whatever you already export from your monitoring system.
Upload via the form below and enter your email, or send directly to the Telegram bot. No registration, no installation, no access to your infrastructure required.
Depending on complexity, within 10 minutes to 48 hours you receive a structured report with findings, risk points, and recommendations — ready to share with your team or client.
Another week, another fire drill. Your team is patching symptoms. The cause is still running.
K-Means groups your 47 incidents into 3 behavioral patterns. Three focused fixes instead of forty-seven.
A feeling, not a fact. Hard to escalate without a number.
Mann-Whitney U test. p < 0.001. Cliff's δ = +0.45. Now you have a fact to bring to the table.
You feel the pattern. You can't prove it. The team calls it bad luck.
Lomb-Scargle detects the hidden cycle with near-zero false-alarm probability. A cron job, a log rotation, a scheduled batch. Fix it once at the root.
You always find the leading indicator after the fact. By then, the damage is done.
Apriori-like lift analysis: lift = 4.2, support = 8.1%, n = 43 event pairs. Set the alert on the leading signal — not on the crash itself.
Backblaze Storage Fleet · 1,057 incidents · 89 days · Q4 2023.
ST8000NM0055 and TOSHIBA MG08ACA16TA fail together in 85% of cases. Apriori lift = 217 — not coincidence. Shared rack or storage pod dependency found.
After December 1: median daily failure rate dropped ~17%. Mann-Whitney U, p = 0.033, Cliff's δ = −0.28 (small but significant). Retiring high-failure drive batches — confirmed by statistics.
94% probability that any drive failure lasts 6+ hours. GPD fit on 105 tail events, 95% CI confirms. Critical input for SLA and redundancy planning.
K-Means + silhouette = 0.484. 1,057 drive failures grouped into 2 patterns by failure gap and timing. Two different root causes — two separate remediation tracks.
All calculations are performed by our custom statistical engine. AI assists with interpretation and validation — it generates no figures of its own.
Here is a real example — one complete audit on the Backblaze public hard drive dataset (Q4 2023).
CSV, JSON, TXT, XLSX · up to 20 MB · no registration required
Free tier: 1 audit per email per week. No credit card required.
Files are permanently removed after your report is delivered.
Your data is not used to train any AI model.
IPs, hostnames, and emails are masked automatically before analysis.
Никаких агентов, VPN-туннелей и доступов к серверам. Вы присылаете только тот файл, который сами решили отправить.
Каждый файл анализируется в изолированной сессии. Между клиентами данные не пересекаются и не накапливаются.
Upload a file and get the PDF by email — or use the Telegram bot. No commitment. No setup. No access to your systems.